Structural latents checkpoint
This commit is contained in:
James Betker
parent
26ceca68c0
commit
ec0ee25f4b
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@ -4,7 +4,7 @@ from time import time
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import torch
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import torchvision
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from torch.utils.data import Dataset
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from kornia import augmentation as augs
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from kornia import augmentation as augs, kornia
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from kornia import filters
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import torch.nn as nn
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import torch.nn.functional as F
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@ -98,10 +98,11 @@ class RandomSharedRegionCrop(nn.Module):
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# 2. Pick a random width, height and top corner location for the first patch.
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# 3. Pick a random width, height and top corner location for the second patch.
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# Note: All dims from (2) and (3) must contain at least half of the image, guaranteeing overlap.
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# 6. Build patches from input images. Resize them appropriately. Apply translational jitter.
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# 7. Compute the metrics needed to extract overlapping regions from the resized patches: top, left,
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# 4. Build patches from input images. Resize them appropriately. Apply translational jitter.\
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# 5. Randomly flip image 2 if needed.
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# 5. Compute the metrics needed to extract overlapping regions from the resized patches: top, left,
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# original_height, original_width.
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# 8. Compute the "shared_view" from the above data.
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# 6. Compute the "shared_view" from the above data.
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# Step 1
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c, d, _ = i1.shape
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@ -122,7 +123,7 @@ class RandomSharedRegionCrop(nn.Module):
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im2_t = random.randint(0, d-im2_h)
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im2_r, im2_b = im2_l+im2_w, im2_t+im2_h
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# Step 6
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# Step 4
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m = self.multiple
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jl, jt = random.randint(-self.jitter_range, self.jitter_range), random.randint(-self.jitter_range, self.jitter_range)
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jt = jt if base_t != 0 else abs(jt) # If the top of a patch is zero, a negative jitter will cause it to go negative.
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@ -139,14 +140,22 @@ class RandomSharedRegionCrop(nn.Module):
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p2 = i2[:, im2_t*m+jt:(im2_t+im2_h)*m+jt, im2_l*m+jl:(im2_l+im2_w)*m+jl]
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p2_resized = no_batch_interpolate(p2, size=(d*m, d*m), mode="bilinear")
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# Step 7
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# Step 5
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should_flip = random.random() < .5
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if should_flip:
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should_flip = 1
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p2_resized = kornia.geometry.transform.hflip(p2_resized)
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else:
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should_flip = 0
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# Step 6
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i1_shared_t, i1_shared_l = snap(base_t, im2_t), snap(base_l, im2_l)
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i2_shared_t, i2_shared_l = snap(im2_t, base_t), snap(im2_l, base_l)
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ix_h = min(base_b, im2_b) - max(base_t, im2_t)
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ix_w = min(base_r, im2_r) - max(base_l, im2_l)
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recompute_package = torch.tensor([base_h, base_w, i1_shared_t, i1_shared_l, im2_h, im2_w, i2_shared_t, i2_shared_l, ix_h, ix_w], dtype=torch.long)
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recompute_package = torch.tensor([base_h, base_w, i1_shared_t, i1_shared_l, im2_h, im2_w, i2_shared_t, i2_shared_l, should_flip, ix_h, ix_w], dtype=torch.long)
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# Step 8
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# Step 7
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mask1 = torch.full((1, base_h*m, base_w*m), fill_value=.5)
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mask1[:, i1_shared_t*m:(i1_shared_t+ix_h)*m, i1_shared_l*m:(i1_shared_l+ix_w)*m] = 1
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masked1 = pad_to(p1 * mask1, d*m)
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@ -171,10 +180,14 @@ def reconstructed_shared_regions(fea1, fea2, recompute_package: torch.Tensor):
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# It'd be real nice if we could do this at the batch level, but I don't see a really good way to do that outside
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# of conforming the recompute_package across the entire batch.
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for b in range(package.shape[0]):
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f1_h, f1_w, f1s_t, f1s_l, f2_h, f2_w, f2s_t, f2s_l, s_h, s_w = tuple(package[b].tolist())
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f1_h, f1_w, f1s_t, f1s_l, f2_h, f2_w, f2s_t, f2s_l, should_flip, s_h, s_w = tuple(package[b].tolist())
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# Unflip 2 if needed.
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f2 = fea2[b]
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if should_flip == 1:
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f2 = kornia.geometry.transform.hflip(f2)
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# Resize the input features to match
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f1s = F.interpolate(fea1[b].unsqueeze(0), (f1_h, f1_w), mode="bilinear")
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f2s = F.interpolate(fea2[b].unsqueeze(0), (f2_h, f2_w), mode="bilinear")
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f2s = F.interpolate(f2.unsqueeze(0), (f2_h, f2_w), mode="bilinear")
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# Outputs must be padded so they can "get along" with each other.
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res1.append(pad_to(f1s[:, :, f1s_t:f1s_t+s_h, f1s_l:f1s_l+s_w], pad_dim))
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res2.append(pad_to(f2s[:, :, f2s_t:f2s_t+s_h, f2s_l:f2s_l+s_w], pad_dim))
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@ -176,3 +176,8 @@ class StructuralBYOL(nn.Module):
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loss = loss_one + loss_two
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return loss.mean()
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def get_projection(self, image):
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enc = self.online_encoder(image)
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proj = self.online_predictor(enc)
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return enc, proj
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@ -3,8 +3,8 @@ import torch
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from models.archs.spinenet_arch import SpineNet
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if __name__ == '__main__':
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pretrained_path = '../../experiments/train_byol_512unsupervised/models/117000_generator.pth'
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output_path = '../../experiments/spinenet49_imgset_byol.pth'
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pretrained_path = '../../experiments/train_sbyol_512unsupervised/models/35000_generator.pth'
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output_path = '../../experiments/spinenet49_imgset_sbyol.pth'
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wrap_key = 'online_encoder.net.'
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sd = torch.load(pretrained_path)
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@ -3,6 +3,7 @@ import shutil
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision
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from PIL import Image
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from torch.utils.data import DataLoader
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@ -10,12 +11,16 @@ from torchvision.transforms import ToTensor, Resize
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from tqdm import tqdm
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import numpy as np
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import utils
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from data.image_folder_dataset import ImageFolderDataset
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from models.archs.spinenet_arch import SpineNet
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# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
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# and the distance is computed across the channel dimension.
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from utils import util
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def structural_euc_dist(x, y):
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diff = torch.square(x - y)
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sum = torch.sum(diff, dim=-1)
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@ -28,6 +33,12 @@ def cosine_similarity(x, y):
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return -nn.CosineSimilarity()(x, y) # probably better to just use this class to perform the calc. Just left this here to remind myself.
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def key_value_difference(x, y):
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x = F.normalize(x, dim=-1, p=2)
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y = F.normalize(y, dim=-1, p=2)
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return 2 - 2 * (x * y).sum(dim=-1)
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def norm(x):
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sh = x.shape
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sh_r = tuple([sh[i] if i != len(sh)-1 else 1 for i in range(len(sh))])
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@ -41,8 +52,8 @@ def im_norm(x):
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def get_image_folder_dataloader(batch_size, num_workers):
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dataset_opt = {
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'name': 'amalgam',
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#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
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'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
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'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
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#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
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'weights': [1],
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'target_size': 512,
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'force_multiple': 32,
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@ -52,7 +63,7 @@ def get_image_folder_dataloader(batch_size, num_workers):
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return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True)
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def create_latent_database(model):
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def create_latent_database(model, model_index=0):
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batch_size = 8
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num_workers = 1
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output_path = '../../results/byol_spinenet_latents/'
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@ -65,7 +76,7 @@ def create_latent_database(model):
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all_paths = []
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for batch in tqdm(dataloader):
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hq = batch['hq'].to('cuda')
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latent = model(hq)[1] # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
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latent = model(hq)[model_index] # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
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for b in range(latent.shape[0]):
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im_path = batch['HQ_path'][b]
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all_paths.append(im_path)
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@ -79,14 +90,8 @@ def create_latent_database(model):
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id += 1
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def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_size):
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def _get_mins_from_comparables(latent, comparables, batch_size, compare_fn):
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_, c, h, w = latent.shape
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lat_dict = torch.load(os.path.join(hq_img_repo, ld_file_name))
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comparables = torch.stack(list(lat_dict.values()), dim=0).permute(0,2,3,1)
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cbl_shape = comparables.shape[:3]
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assert cbl_shape[1] == 32
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comparables = comparables.reshape(-1, c)
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clat = latent.reshape(1,-1,h*w).permute(2,0,1)
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cpbl_chunked = torch.chunk(comparables, len(comparables) // batch_size)
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assert len(comparables) % batch_size == 0 # The reconstruction logic doesn't work if this is not the case.
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@ -94,11 +99,12 @@ def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_si
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min_offsets = []
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for cpbl_chunk in tqdm(cpbl_chunked):
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cpbl_chunk = cpbl_chunk.to('cuda')
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dist = structural_euc_dist(clat, cpbl_chunk.unsqueeze(0))
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dist = compare_fn(clat, cpbl_chunk.unsqueeze(0))
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_min = torch.min(dist, dim=-1)
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mins.append(_min[0])
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min_offsets.append(_min[1])
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mins = torch.min(torch.stack(mins, dim=-1), dim=-1)
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# There's some way to do this in torch, I just can't figure it out..
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for i in range(len(mins[1])):
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mins[1][i] = mins[1][i] * batch_size + min_offsets[mins[1][i]][i]
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@ -106,26 +112,36 @@ def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_si
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return mins[0].cpu(), mins[1].cpu(), len(comparables)
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def find_similar_latents(model):
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def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_size, compare_fn):
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_, c, h, w = latent.shape
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lat_dict = torch.load(os.path.join(hq_img_repo, ld_file_name))
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comparables = torch.stack(list(lat_dict.values()), dim=0).permute(0,2,3,1)
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cbl_shape = comparables.shape[:3]
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comparables = comparables.reshape(-1, c)
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return _get_mins_from_comparables(latent, comparables, batch_size, compare_fn)
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def find_similar_latents(model, model_index=0, lat_patch_size=16, compare_fn=structural_euc_dist):
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img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\adrianna_xx.jpg'
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#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
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hq_img_repo = '../../results/byol_spinenet_latents'
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output_path = '../../results/byol_spinenet_similars'
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batch_size = 1024
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num_maps = 8
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batch_size = 2048
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num_maps = 4
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lat_patch_mult = 512 // lat_patch_size
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os.makedirs(output_path, exist_ok=True)
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img_bank_paths = torch.load(os.path.join(hq_img_repo, "all_paths.pth"))
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img_t = ToTensor()(Image.open(img)).to('cuda').unsqueeze(0)
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_, _, h, w = img_t.shape
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img_t = img_t[:, :, :128*(h//128), :128*(w//128)]
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latent = model(img_t)[1]
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latent = model(img_t)[model_index]
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_, c, h, w = latent.shape
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mins, min_offsets = [], []
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total_latents = -1
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for d_id in range(1,num_maps+1):
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mn, of, tl = _get_mins_from_latent_dictionary(latent, hq_img_repo, "latent_dict_%i.pth" % (d_id), batch_size)
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mn, of, tl = _get_mins_from_latent_dictionary(latent, hq_img_repo, "latent_dict_%i.pth" % (d_id), batch_size, compare_fn)
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if total_latents != -1:
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assert total_latents == tl
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else:
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@ -140,32 +156,37 @@ def find_similar_latents(model):
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print("Constructing image map..")
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doc_out = '''
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<html><body><img id="imgmap" src="source.png" usemap="#map">
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<html><body><img id="imgmap" src="output.png" usemap="#map">
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<map name="map">%s</map><br>
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<button onclick="if(imgmap.src.includes('output.png')){imgmap.src='source.png';}else{imgmap.src='output.png';}">Swap Images</button>
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</body></html>
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'''
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img_map_areas = []
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img_out = torch.zeros((1,3,h*16,w*16))
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img_out = torch.zeros((1, 3, h * lat_patch_size, w * lat_patch_size))
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for i, ind in enumerate(tqdm(min_ids)):
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u = np.unravel_index(ind.item(), (num_maps*total_latents//(32*32),32,32))
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u = np.unravel_index(ind.item(), (num_maps * total_latents // (lat_patch_mult ** 2), lat_patch_mult, lat_patch_mult))
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h_, w_ = np.unravel_index(i, (h, w))
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img = ToTensor()(Resize((512, 512))(Image.open(img_bank_paths[u[0]])))
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t = 16 * u[1]
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l = 16 * u[2]
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patch = img[:, t:t+16, l:l+16]
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img_out[:,:,h_*16:h_*16+16,w_*16:w_*16+16] = patch
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t = lat_patch_size * u[1]
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l = lat_patch_size * u[2]
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patch = img[:, t:t + lat_patch_size, l:l + lat_patch_size]
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img_out[:,:, h_ * lat_patch_size:h_ * lat_patch_size + lat_patch_size,
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w_ * lat_patch_size:w_ * lat_patch_size + lat_patch_size] = patch
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# Also save the image with a masked map
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mask = torch.full_like(img, fill_value=.3)
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mask[:, t:t+16, l:l+16] = 1
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mask[:, t:t + lat_patch_size, l:l + lat_patch_size] = 1
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masked_img = img * mask
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masked_src_img_output_file = os.path.join(output_path, "%i_%i__%i.png" % (t, l, u[0]))
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torchvision.utils.save_image(masked_img, masked_src_img_output_file)
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# Update the image map areas.
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img_map_areas.append('<area shape="rect" coords="%i,%i,%i,%i" href="%s">' % (w_*16,h_*16,w_*16+16,h_*16+16,masked_src_img_output_file))
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img_map_areas.append('<area shape="rect" coords="%i,%i,%i,%i" href="%s">' % (w_ * lat_patch_size,
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h_ * lat_patch_size,
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w_ * lat_patch_size + lat_patch_size,
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h_ * lat_patch_size + lat_patch_size,
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masked_src_img_output_file))
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torchvision.utils.save_image(img_out, os.path.join(output_path, "output.png"))
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torchvision.utils.save_image(img_t, os.path.join(output_path, "source.png"))
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doc_out = doc_out % ('\n'.join(img_map_areas))
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@ -195,12 +216,30 @@ def explore_latent_results(model):
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id += 1
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if __name__ == '__main__':
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pretrained_path = '../../experiments/spinenet49_imgset_byol.pth'
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class BYOLModelWrapper(nn.Module):
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, img):
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return self.wrap.get_projection(img)
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if __name__ == '__main__':
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util.loaded_options = {'checkpointing_enabled': True}
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pretrained_path = '../../experiments/spinenet49_imgset_sbyol.pth'
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model = SpineNet('49', in_channels=3, use_input_norm=True).to('cuda')
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model.load_state_dict(torch.load(pretrained_path), strict=True)
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model.eval()
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#pretrained_path = '../../experiments/train_sbyol_512unsupervised/models/35000_generator.pth'
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#from models.byol.byol_structural import StructuralBYOL
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#subnet = SpineNet('49', in_channels=3, use_input_norm=True).to('cuda')
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#model = StructuralBYOL(subnet, image_size=256, hidden_layer='endpoint_convs.3.conv')
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#model.load_state_dict(torch.load(pretrained_path), strict=True)
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#model = BYOLModelWrapper(model)
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#model.eval()
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with torch.no_grad():
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find_similar_latents(model)
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#create_latent_database(model, 0) # 0 = model output dimension to use for latent storage
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find_similar_latents(model, 0, 8, structural_euc_dist) # 1 = model output dimension to use for latent predictor.
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@ -292,7 +292,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_512unsupervised.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_sbyol_512unsupervised.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user